Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

In most batch processes, the correlations of process variables present multi‐stage characteristic as the process progress and operating conditions change. The methods building a local model at each stage ignore the potential correlations among stages, resulting in poor quality prediction of batch process. To solve this problem, a batch process quality prediction method based on multi‐stage fusion regression network (MSFRN) is proposed. First, the affine propagation clustering (AP) algorithm is used to automatically divide the stages for batch process without relying on prior knowledge. Second, the input reconstruction error and quality prediction error are organically combined to develop a stacked isomorphic and quality‐driven autoencoder (SIQAE) for each stage, which fully extracts the quality‐related features for each stage while reducing the input cumulative loss. Then, the self‐attention mechanism is used to integrate the quality‐related features of each stage so as to obtain global features which consider the correlations among stages. Finally, the global features are input into the fully connected regression layer to predict the quality variables of batch process. The effectiveness of the proposed method was verified by applying on penicillin fermentation process.

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“…The penicillin fermentation process has been widely used in the modelling, monitoring and control of batch processes. [ 49–51 ]…”

Section: Case Studymentioning

confidence: 99%

Multi‐stage fusion regression network for quality prediction of batch process

Yao

Zhao

et al. 2023

Can J Chem Eng

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“…They are typical batch processes. A modified multi-stage GNPE method (Yao et al, 2021), Multiway Slow Feature Analysis (MSFA; Shumei Zhang and Zhao, 2018), MNPE (Sun et al, 2018), and MPCA (Zhaomin et al, 2014) are used for comparison.…”

Section: Case Studiesmentioning

confidence: 99%

Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm

Zhao

Liu

Hui

2022

Transactions of the Institute of Measurement and Control

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Batch process is an important type of industrial production process, and the process mechanism is complex. It is difficult to accurately describe the dynamic changes of the production process of multi-stage time-varying batch process. In addition, the data of batch process contain not only global information but also local information. The traditional neighborhood preserving embedded algorithm is used to maintain the local geometric structure of data while ignoring the global information, and the extracted latent variables cannot fully characterize batch process. Therefore, we propose a multi-stage optimization regularized neighborhood preserving embedding (ORNPE) algorithm. First, the multiple process stages are separated by affinity propagation (AP) algorithm. Second, based on maintaining local information of neighborhood preserving embedding algorithm, slow feature analysis algorithm is used to extract dynamic time-varying global information. Then, cross-entropy is used to optimize the global information, and the extraction ability of the global information is improved. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The effectiveness and advantages of the proposed algorithm based on monitoring strategy are illustrated by the penicillin fermentation process and a semiconductor industry process.

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“…As the dimensionality increases, the Euclidean distance does not apply. In the case of uneven manifolds or losing data, the nearest neighbor points selected based on the Euclidean distance are not accurate (Yao et al, 2022). The cosine similarity is more suitable for outliers and data sparse problems, and easier to select accurate neighbor points.…”

Section: Cosine Similaritymentioning

confidence: 99%

Dual-weight local linear embedding algorithm based on adaptive neighborhood

Zhang

Gao-zhiwei

et al. 2022

Transactions of the Institute of Measurement and Control

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Local linear embedding (LLE) algorithm mainly depends on local structures to extract significant features; however, the local structures are sensitive to the selection of neighborhood parameters. To solve this problem, the dual-weight local linear embedding algorithm based on adaptive neighborhood (AN-DWLLE) is proposed. First, the neighborhood parameter is adaptively estimated by computing the distances between the inquiry point and the centers of the inquiry point’s candidate neighborhoods. Then, the neighbor sequence and the local linear structures are integrated to construct the robust local structure that can be used to extract the significant features. A large number of experiments are performed on two bearing data sets. The experimental results illustrate that, compared with other related methods, the AN-DWLLE algorithm can extract more salient features and has greater dimensionality reduction effect.

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Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

Cited by 5 publications

References 34 publications

Multi‐stage fusion regression network for quality prediction of batch process

Multi‐stage fusion regression network for quality prediction of batch process

Fault monitoring of batch process based on multi-stage optimization regularized neighborhood preserving embedding algorithm

Dual-weight local linear embedding algorithm based on adaptive neighborhood

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